Solar energy systems have emerged as a viable source of renewable energy over the past two or three decades, and are now widely used for a variety of industrial and domestic applications. This paper shows the potential system benefits of simple tracking solar system using a stepper motor and light sensor. This method is increasing power collection efficiency by developing a device that tracks the sun to keep the panel at a right angle to its rays. Such systems are based on a solar collector, designed to collect the sun’s energy and to convert it into either electrical power or thermal energy The output power produced by high-concentration solar thermal and photovoltaic systems is directly related to the amount of solar energy acquired by the system, and it is therefore necessary to track the sun’s position with a high degree of accuracy. The power developed in such applications depends fundamentally upon the amount of solar energy captured by the collector, and thus the problem of developing tracking schemes capable of following the trajectory of the sun throughout the course of the day on a year-round basis has received significant coverage in the literature. A solar tracking system is designed, implemented and experimentally tested. The design details and the experimental results are incorporated in this paper.
Photovoltaic systems are so versatile that it can supply any electric power need and are used for numerous applications. Recent advancements in efficiency and cost reduction have made photovoltaic systems economically competitive with traditional power sources. This paper presents an intelligent method of peak power point tracking for photovoltaic systems based on tracking the peak power point by measuring the voltage and current of the solar array to control a buck-boost DC to DC converter. Result analysis shows that the neuro-fuzzy controller can deal with different load and weather conditions and deliver more power from the photovoltaic systems. To increase the efficiency of PV panels, it must operate around the peak power point which is influenced by cell temperature and sun irradiation. A controller therefore is needed to find the peak power point and control PV output voltage according to peak power point voltage. The aim of the paper is to design and analyze neural fuzzy controller for controlling the PV system output voltage.
This Paper presents a Nonlinear models for the left and right super heater tubes of the superheated steam control process of thermal power plant using neural networks. The real time data which is obtained by measuring the process variables at frequent intervals for one day is used to train a neural network. The network was validated and tested. The models developed faithfully duplicate the superheated steam temperature control process. These models can be used for the design of controller for super heated steam temperature control process.
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